Systems and methods for interference cancellation in a radio receiver system

ABSTRACT

A system and method for interference cancellation is provided to cancel/greatly reduce the interference of a wireless network. The interferers are separated from a desired signal using independent component analysis by hypothesizing the transmitting sequence. An optional whitening filter is used after the signal separation to improve the signal conditioning. The separated signal is processed by a second pass channel estimation to improve the signal channel estimation and is fed to a Maximum Likelihood Sequence Estimation (MLSE) algorithm, such as a Viterbi algorithm, for signal detection.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 60/467,446 entitled “A Method for InterferenceSeparation/Cancellation for Wireless Network”, filed May 2, 2003, whichis incorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This application relates to interference cancellation, specifically assuch interference cancellation applies to wireless telecommunications.

2. Description of the Related Art

When observing a cocktail party, a few characteristics relating to thesound are readily apparent. One is that individual conversations can beheard by the participants of those individual conversations and by thosein close proximity thereto. The second is that to a person not takingpart in a conversation and also not within close proximity to anyindividual conversation, the collective noise of the many conversationscan be heard, but no individual conversation can be discerned from thegeneral noise. In the first instance, the conversations can be heardbecause the signal (i.e., voice) to interference ratio is strong withinclose proximity to the speaker. In the second instance, the signal tointerference ratio is low, and therefore interference from multipleconversations masks the individual conversations. Both observations,however, highlight the role that interference can have on sound quality.

The same phenomenon is applicable to wireless communications.Interference degrades performance, and unless the signal to interference(noise) ratio is sufficiently high, no particular signal can bediscerned. In simplistic terms, that means the voice on the other end ofa cellular telephone call may not be heard by a receiver of the call ifthe interference is too strong. Accordingly, cellular and other wirelesscommunications systems have been designed to insure that the signalreceived by the wireless device is sufficiently strong relative to theinterference such that the voice (and data) can be heard and understood.

Mobile telecommunication service providers and vendors are constantlystriving to improve the quality and performance of cellular telephonecommunications. One such area of focus is the reduction of theinterference caused from either (1) adjacent frequency carriers of acellular base station, known by those skilled in the art as “adjacentchannel interference,” or (2) adjacent cellular base stations operatingat the same frequency carrier, known by those skilled in the art as“co-channel interference.” The problem of interference is exacerbatedwhen the cellular system is operating within a tight frequency spectrum,especially and including GSM (Global System for Mobile Communications)with a high traffic load. At the present time, there is nothingimplemented in a mobile handset that will cancel or greatly reduceeither the adjacent channel or co-channel interference. Accordingly,communications utilizing such handsets are not able to optimize the useof the available frequency spectrum.

As will be appreciated by those skilled in the art, similar to theso-called cocktail party problem, the interference observed by acellular telephone receiver is a summation of individual interferencefrom each of a variety of sources. Such sources of interference include,among other things, adjacent channels within a base station andco-channel and adjacent channels from surrounding base stations, and mayinclude interference from other sources as well. Because nothing isknown about the individual signals causing the interference, it isdifficult to extract the original voice signal; hence the cocktail partyproblem arises.

There are known techniques for reducing or canceling the noise incertain circumstances. For example, in analyzing the summation ofmultiple interference signals, it is possible to determine whether oneparticular interference source is dominant over the others contributingto the summation. The term “dominant interference ratio” or DIR, is usedto describe the ratio of interference caused by an individualinterference source to the summation of interference from all theinterference sources combined. In the case where there is a DIR muchgreater than one (1), there is typically a dominant interferer. In thecase where the DIR is less than or approximately equal to one (1), thenthere is no dominant interferer.

Manufacturers and others in the telecommunications industry have beenaddressing the interference problem by designing interferencecancellation features into their systems. One way to combat theinterference is to use frequency hopping where the interference at acertain frequency will be distributed among multiple frequencies insteadof being focused on one frequency. See, for example, Olofsson,“Interference Diversity Gain in Frequency Hopping GSM,” 1995 IEEE, 45thVehicular Technology Conference, Vol. 1, 25-28 Jul. 1995, pages 102-106.Such interference averaging schemes are marginally helpful but do notquite reduce or cancel the interference source to acceptable levels.Another example of interference mitigation is to use a technique knownin the art as “Interference Rejection Combining” (IRC). See, forexample, Craig et al., “A System Performance Evaluation of 2-BranchInterference Rejection Combining,” 2002 IEEE 56th Vehicular TechnologyConference, 2002. Proceedings. VTC 2002-Fall. Vol. 3, 24-28 Sep. 2002,pages 1887-1891. Each of the above-cited references is herebyincorporated by reference in its entirety. IRC is marginally effectivewhere there is a dominant interference source, i.e., those systems wherethe DIR is much greater than one (1). For those situations where thereis not a dominant interference source, the interference suppressiontechniques being developed by those skilled in the art are mostlyineffective and the benefits realized pale in comparison to the cost ofimplementation.

In various interference suppression techniques outside of the cellulartelephone industry, there exist various methodologies relating generallyto interference suppression. However, those prior art techniques areonly applicable in a situation where there is a multiple antenna system.Using those known techniques, those skilled in the art will appreciatethat if there is a series of N antennas forming a system, interferencecan be suppressed for all but one (N−1) of those antennas. This isbecause at least two actual signals must be observed for the knownsuppression algorithms to be applicable. In the context of a mobilehandset in a cellular telephone system, however, there is a singleantenna, not multiple antennas, which is experiencing the interference.Therefore, using conventional known techniques, there is no methodologyto suppress multiple interferers experienced by the single antenna in awireless device.

One option considered in the industry would be to add a second antennain each handset and then apply cancellation techniques to one of thoseantennas. While this solution is associated with increases cost, weightand complexity, single antenna interference cancellation with moderateinterference suppression is a preferred solution.

U.S. patent application of Meyer et al., Publication No. U.S.2002/0141437 (the “Meyer Application”) addresses a method forinterference suppression for TDMA and/or FDMA transmissions, with anarbitrary number of receive antennas. The Meyer Application discloses areal value modulation technique wherein the real component of a receivedsignal is separated from an imaginary component of the received signal.The measured received signal is phase shifted from the transmit signaldue to channel oscillation and other factors. The received signal isthen projected back onto the real axis. The methodology assumes signaland part of interference are orthogonal and, as such, the real andimaginary part of the signal can be exploited to cancel theinterference. The inventor believes that the methodology described inthe Meyer application is sensitive to the actual data comprising thesignal, including some of the embedded data signals such as the trainingsequence. Also, the complexity of the calculations appears to besignificantly higher than the complexity of the present invention.Because of this, the technique is unreasonable to implement in alarge-scale wireless telecommunication system.

Another method of interference cancellation, involving at least 2antenna systems, is combining algorithms, including a “switchingcombining algorithm” wherein one of the signals is ignored at any givenpoint in time, and an “interference ratio combining algorithm” whereineach signal is weighted in accordance with its signal-to-interferenceratio. Again, the problem with these types of combining algorithms isthat they require the observations to be independent in order to providereasonable gain. As will be appreciated by those skilled in the art, theability to receive highly independent receive signals within the smallfootprint of a handset is a very challenging task.

The inventor has developed a solution to the single and multiple antennaproblems with the present invention. While the present invention mayutilize any number of existing known algorithms, including the combiningalgorithms or the real-value modulation set forth above, the methodologyof the present invention preferably uses independent component analysis,or ICA, and applies it to the experience of the single antenna wirelessdevice. ICA is known by those skilled in the art as a statistical modelwhere the observed data is expressed as a linear combination ofunderlying latent variables. In ICA, the latent variables are assumednon-Gaussian and mutually independent, which assumptions apply to awireless device antenna as well. The task is to find out both the latentvariables and the mixing process. The ICA model used isx=Aswhere x=(x₁, x₂, . . . x_(n)) is the vector of observed random variablesand s=(s₁, s₂, . . . s_(n)) is the vector of statistically independentlatent variables called the independent components, and A is an unknownconstant mixing matrix. ICA is very closely related to blind sourceseparation (BSS), where a “source” means the original signal, such asthe original voice transmission (or the speaker at a cocktail party).Independent component analysis is described in more detail in theliterature, for example in Hyvärinen and Oja, “Independent ComponentAnalysis: A Tutorial,” Helsinki University of Technology, Laboratory ofComputer and Information Science, April 1999, and Bingham and Hyvärinen,“A Fast Fixed-Point Algorithm for Independent Component Analysis ofComplex Valued Signals,” Neural Networks Research Centre, HelsinkiUniversity of Technology, 19 Jan., 2000, each of which is incorporatedby reference.

In conjunction with the preferred embodiment of the present invention,the inventor has created new and non-obvious whitening filter which,when implemented in accordance with the parameters set forth herein,further improves performance of the interference cancellation process.

Accordingly, the present invention solves a major problem ofinterference in the wireless communications industry. The methodologythat has been developed may be implemented in a wireless device tosuppress interference signals and improve the signal quality of thereceived signal. The invention permits a substantial reuse offrequencies in wireless communications, thereby increasing the capacityof the network significantly. The methodology of the present inventionis applicable to all wireless technologies, including TDMA, CDMA, GSM,EDGE, WCDMA, 802.11 and 802.16, using any of a variety of modulationtechniques, including GMSK, QPSK, 8PSK, and OFDM.

The present invention may be embodied in a receiver used in suchwireless communications. The receiver preferably implements the presentinvention in conjunction with a separation algorithm optimized for theparticular technology in order to increase the quality of the receivedsignal. Such a receiver would then be useful in a system in which theco-channel interference and adjacent channel interference may beincreased in order to obtain significant capacity increases in thenetwork.

The present invention also may be embodied in the uplink stage of nmulti-receivers in a Base Station Transceiver Subsystem (BTS). As such,not only could the interference cancellation algorithm be utilized in aconventional manner for n−1 of those multiple receivers, but rather thepresent invention enables cancellation of interference for all n of suchindividual receive antennas in the BTS.

While the present invention has been described in terms of cellularmobile radio telecommunications systems, the invention is applicableacross a broad range of applications and devices wherein single ormultiple antennas receive signals that are susceptible to various typesof interference.

As set forth in the detailed description of the preferred embodiment,including the graphical results set forth therein, the present inventionhas achieved results far exceeding those which would be reasonablyexpected by those skilled in the art attempting to solve theinterference problem in wireless communications.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made in detail to the preferred embodiments of thepresent invention, examples of which are illustrated in the accompanyingdrawings, and wherein like reference numerals identify like or similarstructures or processes and wherein:

FIG. 1 is a block diagram of a portion of a wireless communicationsystem showing the main functional components of a handset constructedin accordance with the teachings of the present invention;

FIG. 2 is an example of a typical GSM data stream in which the TrainingCode Sequence is shown;

FIG. 3 is a block diagram of the receiver illustrated in FIG. 1constructed in accordance with the teachings of the present invention;

FIG. 4 is a block diagram of the signal conditioner functional componentof the receiver illustrated in FIG. 3;

FIGS. 5 and 6 are graphs showing results obtained from numericalmodeling of the techniques developed as part of the present invention;

FIG. 7 is a diagram of the signal-whitening filter of the signalconditioner functional component illustrated in FIG. 4;

FIG. 8 is a flow diagram illustrating operationally the receiver in apreferred single antenna environment; and

FIG. 9 is a flow diagram illustrating the post-processing functionalityof the receiver in a preferred single antenna environment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The exemplary embodiments described herein are preferably applied tointerference cancellation for a wireless downlink channel, i.e., achannel conveying information from a transceiver or base station of awireless cellular system to a receiver, and to apparatus and methodsthat may be implemented in a wireless communications terminal, forexample, a cellular radiotelephone, wireless capable personal digitalassistant (PDA) or similar communications device. The present inventionis also applicable in an alternative embodiment wherein multiple receiveantennas in a wireless cellular system, e.g., a base station receiver,desire interference cancellation in order to enhance signal quality andcapacity. It will be further appreciated, however, that the presentinvention may be used in other environments, e.g., in other types ofwireless transmitter applications or in wireless receiver applicationsin which traditional interference cancellation techniques are notavailable or ineffective. The system and method provides significantgain over all previous methods with no limitation to the frequency typeor specific interference profile such as the ratio of the dominantinterferer to the other interferers. It is possible to separate theinterferers without prior knowledge of the specific parameters of theinterferers and with much less complexity than conventional jointdemodulation detection methods. The algorithm employed is considered ablind estimation approach which makes it attractive for wireless devicesthat have limited power resources.

This application utilizes various acronyms throughout the specificationand drawings. For convenience, unless otherwise set forth in the text,those acronyms are defined as follows:

AFC—Automatic Frequency Correction

AWGN—Additive White Gaussian Noise

BTS—Base Transceiver Subsystem (or Base Station)

BER—Bit Error Rate

CDMA—Code Division Multiple Access

C/I—Carrier to Interferer Ration Ratio

DFSE—Decision Feedback Sequence Estimator

DIR—Dominant Interferer Ratio

DSP—Digital Signal Processing

EDGE—Enhanced Data rates for Global Evolution

FDMA—Frequency Division Multiple Access

GMSK—Gaussian Minimum Shift Keying

8PSK-8 (constellation) Phase Shift Keying

GPRS—Generalized Packet Radio System

GSM—Global System for Mobile Communications

MLSE—Maximum Likelihood Sequence Estimation

OFDM—Orthogonal Frequency Division Multiplexing

QPSK—Quaternary Phase Shift Keying

RSSE—Reduced State Sequence Estimation

SAIC—Single Antenna Interference Cancellation

TDMA—Time Division Multiple Access

TSC—Training Sequence Code

WCDMA—Wide-band CDMA

With reference to FIG. 1, there is shown a mobile telecommunicationssystem 200 with a mobile device 203 and multiple cellular antennatransceivers 201 a, 201 b . . . 201 n (generically referred tohereinafter as 201). The present invention may be implemented in anytype of mobile telecommunications system 200, including a GSM/GPRStelecommunications system in which there is a mobile receiver 213subject to interference. Implicit in the adaptability to various typesof networks is the adaptability to the various encoding schemesassociated with those networks, i.e., GMSK for GSM. The methods setforth as part of the invention herein are applicable for other codingschemes as well, including QPSK (TDMA), 8PSK (EDGE and WCDMA), OFDM(e.g. 802.11; 802.16). The whitening filter and filtering methodology ofthe present invention is valid for single-valued modulation such as GSMand BPSK and other single-valued modulation techniques. However, as willbe understood by those in the art, coding schemes such as QPSK and 8-PSKuse vector value modulation and hence may not achieve as significant abenefit from the inherent diversity nature of the whitening filter andfiltering methodology presented herein.

In the description of a preferred embodiment in which the single antennainterference cancellation occurs in the mobile device 203, the cellularantenna transceivers 201 require no modification from that known in theart. Each of the cellular antenna transceivers 201 are typicallyoperated in sectors 202 a, 202 b, 202 n (generally 202), and may, forexample, comprise three sectors per cellular antenna transceiver 201.Each of the sectors transmits to a mobile device 203 in accordance withits own frequency mapping and its own training sequence code (TSC).

As illustrated in FIG. 2, the TSC 102 is typically an 26 bit codeembedded in a transmission burst 100 from cellular antenna transceivers201 and used as an identifier for all mobile devices 203 in a particularsector 202. FIG. 2 illustrates a typical burst configuration. Aparticular TSC 102 is typically assigned to a sector or a cell. Sincethere are a total of 8 TSC codes, cells within the network reuse TSCcodes. In a typical GSM configuration, each transceiver 201 transmits aburst every 4.7 milliseconds, or over 200 bursts per second. The TSC 102may be embedded in the middle of the transmission burst 100 along withcontrol bits 104 and data bits 108, 110 in accordance with system designand/or industry standards. In a hand-off to the next antenna, the TSC102 parameter for the new sector 202 is conveyed to the mobile device203 along with the assigned frequency and time slot. As such, the TSC102 is a known parameter in any mobile telecommunications system 200 andall types of mobile devices 203 are capable of identification thereof.

The present invention utilizes the TSC 102 in a novel and non-obviousmanner in performing the antenna interference cancellation algorithm.That algorithm as applied to a single antenna system will be describedbelow, followed by a detailed description of a preferred embodiment ofthat algorithm within a mobile telecommunications system 200 and mobiledevice 203.

The received desired signal output at a receive filter (RX filter 222 ofFIG. 3) within the mobile device 203 for the i antenna branch can bewritten as:

$\begin{matrix}{{{r_{d\mspace{14mu} i}(k)} = {\sum\limits_{n = 0}^{L_{id} - 1}{{h_{id}(n)}{d\left( {k - n} \right)}}}}{{{r_{{int}\mspace{14mu}{ij}}(k)} = {{\sum\limits_{n = 0}^{L_{ij} - 1}{{h_{ij}(n)}{I_{j}\left( {k - n} \right)}\mspace{14mu} j}} = 1}},\ldots\mspace{14mu},N}} & (1)\end{matrix}$

where

r_(di)(k) is the desired signal received for the ‘i’ branch

r_(int ij)(k) is the received ‘j’ interferer signal received at the ‘i’branch.

d(k) is the desired transmitted data symbols

I_(j)(k) is the interferer transmitted data symbols from ‘j’ interfererat the ‘i’ branch.

h_(ij)(n) is the effective channel impulse of the ‘j’ interferer for the‘i’ branch.

h_(id)(n) is the effective channel impulse of the desired signal for the‘i’ branch.

L_(id) is the length of the effective channel impulse response of thedesired signal

L is the length of the effective channel impulse response of the ‘j’interferer

The effective channel impulse response is defined as the convoluted,transmitted received filters and the media response and can berepresented by the following equation:h _(eff)(n)=tx(n)*C(n)*rx(n)  (2)wherein

-   -   tx(n) is the filter response of the linearized GMSK filter as        defined in the 3GPP, GERAN Standards (which are hereby        incorporated by reference);    -   rx(n) is the receiver filter; and    -   C(n) is the media response

For generality, the inventor assumes that there are N interferers and Mreceiver branches. Moreover, for simplicity, the length of the effectivechannel impulse response of the desired signal and the interferer signalis assumed to be the same for the different receiver branches.

Description of the Separation Algorithm:

A general algorithm for separating multiple received signal (desired andinterferers) based on the composite the received signals will bedescribed. Assume M received signal through M branch antennas based onthe desired signal and N interferers. We can express the received signalas follow:

$\begin{matrix}{{{r_{1}(k)} = {{h_{1\; d} \otimes {d(k)}} + {h_{11} \otimes {I_{1}(k)}} + {h_{12} \otimes {I_{2}(k)}} + {\ldots\mspace{20mu}{h_{1N} \otimes {I_{N}(k)}}} + {w_{1}(k)}}}{{r_{2}(k)} = {{h_{2d} \otimes {d(k)}} + {h_{21} \otimes {I_{1}(k)}} + {h_{22} \otimes {I_{2}(k)}} + {\ldots\mspace{20mu}{h_{2N} \otimes {I_{N}(k)}}} + {w_{2}(k)}}}\vdots{{r_{M}(k)} = {{h_{Md} \otimes {d(k)}} + {h_{M\; 1} \otimes {I_{1}(k)}} + {h_{M\; 2} \otimes {I_{2}(k)}} + {\ldots\mspace{20mu}{h_{MN} \otimes {I_{N}(k)}}} + {w_{M}(k)}}}} & (3)\end{matrix}$Where

r_(i)(k) is the received signal for branch ‘i’

w_(i)(k) is the AWGN signal received in the branch ‘i’; and

{circle around (x)} indicates convolution operator

Note r₁(k) . . . r_(M)(k) may have some degree of correlation and thatit is not necessary for such receive signals to be totallyde-correlated.

For two diversity branches, equation (3) can be written asr ₁(k)=r _(d 1)(k)+r _(int 1)(k)r _(int j)(k)r ₂(k)=r _(d 2)(k)+r _(int 2)(k)  (4)wherein:

$\begin{matrix}{{{r_{d\mspace{14mu} i}(k)} = {\sum\limits_{n = 0}^{L_{d} - 1}{{h_{i}(n)}{d\left( {k - n} \right)}}}}{{r_{{int}\mspace{14mu} i}(k)} = {{\sum\limits_{j = 1}^{N}{\sum\limits_{n = 0}^{L_{j} - 1}{{h_{ij}(n)}{I_{j}\left( {k - n} \right)}}}} + w_{i}}}} & (5)\end{matrix}$and wherein r_(di)(k) is the desired signal received at the “i” branch,r_(int i)(k) is the composite interference and noise signal received atthe “i” branch, and n is the number of interferers.

In a preferred embodiment, the separation algorithm is carried over thetraining sequence. That is, in a single antenna system, the TSC 102 isused to create a hypothesized received signal and thereafter thathypothesized receive signal is used as a second received signalotherwise not available in a single antenna system. However, generally,the separation algorithm is preferably applied to all received signals.Specifically, the hypothesized received signal is constructed afterdecoding the soft bits from an equalizer. The decoded data bits shouldbe very close, if not identical, to the transmitted data d(k). Thedecoded data is interleaved and modulated, to produce the signal d^(k),as if it was to be transmitted. While it is preferred to use thereceived symbols in the TSC 102 location of the transmission burst 100,for multiple sensors applications (that is, multiple receive branches),the separation algorithm may be carried over any portion of the receivedburst. For example, certain portions of a transmission burst 100 may bepre-defined and used in lieu of or in addition to the TSC 102.

A preferred separation algorithm to be used is based on the centrallimit theory. As will be appreciated by those skilled in the art, unlessthe independent components comprising interfering signals are Gaussian,the sum of those independent components tends to be more Gaussian thaneach of the individual components. For the purposes of thisillustration, it is assumed that distribution of the original (orseparated) signals is non-Gaussian and mutually independent, which istypically true in a GSM environment with the exception of the simulcast.Moreover, there are many iterative algorithms that may be used, with therate of convergence and complexity being factors in an implementationdecision. Note that the duration of a GSM transmission burst 100 isabout 0.57 msec and therefore it may be safely assumed that the channeldoes not change over the course of the burst. There are two otherassumptions in the separation algorithm, namely, that at most, one ofthe independent components, d_(j), may be Gaussian and the unknownmixing matrix H must be of full rank and assumed to be constant.

In a system wherein r=(r₁, r₂ . . . r_(M)) is the vector of the observedrandom variables and d=(d₁, d₂ . . . , d_(M)) is the vector ofstatistically independent signals, r and d are combined. The covariancematrix of the observed random variables is given by:

$\begin{matrix}{{E\left( {rr}^{H} \right)} = \begin{pmatrix}C_{11} & \ldots & C_{1M} \\\vdots & ⋰ & \vdots \\C_{1M} & \; & C_{MM}\end{pmatrix}} & (6)\end{matrix}$Where r^(H) stands for the Hermitian, or conjugate transpose, of r. Thecovariance matrix is used to whiten the observed random variables toproduce a new set of normalized random variables r=( r ₁, r ₂ . . . r_(M)) with zero mean and unit variance. Principle component analysis maybe also used to whiten the observed random variables. Therefore,C_(ij)=E( r _(i) r _(j) ^(H))=I is the covariance of random variables r_(i) and r _(j). It is desired to find a separation matrix S such that:r_(clean)=S^(H) r  (7)As will be appreciated by those skilled in the art, the kurtosis, orfourth order statistics, is a measure of non-Gaussianity of a randomvariable. There are 2⁴ ways to define the kurtosis. The algorithm isused for separating the linearly mixed signals. Among the kurtosisfunctions commonly used, the following choices are preferred:kurt({circumflex over (d)})=E(|{circumflex over (d)}| ⁴)−E({circumflexover (d)}{circumflex over (d)}*)E({circumflex over (d)}{circumflex over(d)}*)−E({circumflex over (d)}{circumflex over (d)})E({circumflex over(d)} {circumflex over (d)}*)−E({circumflex over (d)}{circumflex over(d)}*)E({circumflex over (d)}*{circumflex over (d)})kurt({circumflex over (d)})=E({circumflex over (d)} ⁴)−2  (8)Note that if d is Gaussian, the Kurt vanishes—which is intuitive. Thekurtosis is used in a Fast Fixed Point algorithm to calculate theupdated value at each iteration. See, e.g., Hyvärinen and Oja,“Independent Component Analysis: A Tutorial,” Helsinki University ofTechnology, Laboratory of Computer and Information Science, April 1999.Let s_(p)(0) be the starting initial vector. Using the Fast Fixed pointalgorithm and using the first kurtosis definition in (6), the updatedvalue is given by:s _(p)(m+1)=s _(p)(m)+2*E{ r (s _(p) ^(H)(m) r )*−2s _(p) ^(H)(m))|(s_(p) ^(H)(m) r )|²  (9)The new value is normalized

$\begin{matrix}{{s_{p}\left( {m + 1} \right)} = \frac{s_{p}\left( {m + 1} \right)}{{{s_{p}\left( {m + 1} \right)}}}} & (10)\end{matrix}$A convergence is reached when the condition |s_(p) ^(H)(m+1)s_(p)(m)|≈1is met, otherwise iteration continues. To avoid convergence to thepreviously found s_(p), a deflation method based on Gram-Schmidt isapplied where the estimated vector s_(p)(m+1) at each iteration issubtracted from the previously ‘p’ found vectors. To estimate the Nindependent components, the algorithm needs to be executed N times. Thealgorithm can be prevented from converging into the previously foundcomponent by selecting a new starting vector that is orthogonal to thepreviously found ones.

The other signals are separated using a deflation method. The deflationtends to separate the independent component in the order of decreasingnon-Gaussianity, which is often equal to decreasing the importance ofthe independent signals.

$\begin{matrix}{{s_{p}\left( {m + 1} \right)} = {{s_{p}\left( {m + 1} \right)} - {\sum\limits_{j = 1}^{P}{s_{j}s_{j}^{H}{s_{p}\left( {m + 1} \right)}}}}} & (11)\end{matrix}$Description of the Temporal Whitening filter:

The key equations of a whitening filter developed as part of the presentinvention will now be described.

Equation (1) may be re-written as

$\begin{matrix}{{r_{z}(k)} = {{\sum\limits_{n = 0}^{L - 1}{{h_{d}(n)}{d\left( {k - n} \right)}}} + {u(n)}}} & (12)\end{matrix}$where u(n) is the contribution of the interference including the thermalnoise and is mostly color noise, and r_(z)(k) is the input signal to awhitening filter

The color noise, u(n), as a random variable can be predicted using a onestep predictor as

$\begin{matrix}{{\hat{u}\left( {n❘{n - 1}} \right)} = {\sum\limits_{l = 1}^{M}\;{w_{l}{u\left( {n - l} \right)}}}} & (13)\end{matrix}$wherein M is the order of the predictor and wl is the filter coefficientof the forward predictor. Therefore, the forward prediction error isgiven bye(n)=u(n)−û(n|n−1)  (14)

The detail of the known filter equation is given in Adaptive FilterTheory, Prentice Hall, Inc., by Simon Haykin, which is herebyincorporated by reference. After a detailed and lengthy manipulation, itcan be shown that

$\begin{matrix}{{r_{z}(k)} = {{\sum\limits_{j = 0}^{{M + L} = 1}\;{{h_{fil}(j)}{d\left( {k - j} \right)}}} + {\sum\limits_{l = 1}^{M}\;{w_{l}{r_{z}\left( {k - l} \right)}}} + {e(k)}}} & (15)\end{matrix}$where h_(fil) is the filtered extended channel estimate for the desiredchannel and e(k) is a white Gaussian noise random variable. Moreover,h_(fil) can be given byh _(fil) =h _(d) *f  (16)where

${f(l)} = \left\{ \begin{matrix}1 & {l = 0} \\{- w_{l}} & {l > 0}\end{matrix} \right.$At this point, the goal is to find joint estimate of the channelcondition of the desired signal and the parameters w_(l), thatapproximate the color noise.

Ignoring the noise term, Equation 15 can be put in the following matrixform

$\begin{matrix}{\begin{bmatrix}{r_{z}(k)} \\{r_{z}\left( {k + 1} \right)} \\\vdots\end{bmatrix} = {\begin{bmatrix}{d(k)} & {d\left( {k - 1} \right)} & {d\left( {k - \left( {M + L - 1} \right)} \right)} & {r_{z}\left( {k - 1} \right)} & {r_{z}\left( {k - 2} \right)} & \ldots & {r_{z}\left( {k - L} \right)} \\{d\left( {k + 1} \right)} & \ldots & \ldots & \ldots & \ldots & \ldots & {r_{z}\left( {k - L + 1} \right)} \\\vdots & \ldots & \; & \; & \; & \; & \;\end{bmatrix}\begin{bmatrix}{h_{fil}(0)} \\{h_{fil}(1)} \\\ldots \\{w_{l}(1)} \\\ldots \\{w_{l}(M)}\end{bmatrix}}} & (17)\end{matrix}$r(k)=d(k−1)h  (18)where

$\begin{matrix}{{d\left( {k - 1} \right)} = \left\lbrack {{d(k)}\mspace{14mu}{d\left( {k - 1} \right)}\mspace{14mu}\ldots} \right.} & (19) \\{\left. {{d\left( {k - \left( {M + L - 1} \right)} \right)}\mspace{14mu}{r_{z}\left( {k - 1} \right)}\mspace{14mu}{r_{z}\left( {k - 2} \right)}\mspace{14mu}\ldots\mspace{20mu}{r_{z}\left( {k - L} \right)}} \right\rbrack\left. \quad \right\rbrack} & \; \\\text{and} & \mspace{11mu} \\{h = \begin{bmatrix}{h_{fil}(0)} \\{h_{fil}(1)} \\\ldots \\{w_{l}(1)} \\\ldots \\{w_{l}(M)}\end{bmatrix}} & \;\end{matrix}$using the Minimum Mean Square Estimate (MMSE) for equation 18, we obtainP=R h  (20)h=R ⁻¹ P  (21)WhereR=E{d(k−1)d(k−1)^(H)}andP=E{d(k−1)r(k)^(H)}and

$\begin{matrix}{h = \begin{bmatrix}{h_{fil}(0)} \\{h_{fil}(1)} \\{h_{fil}\left( {{M + L} = 1} \right)} \\\ldots \\{w_{l}(1)} \\\ldots \\{w_{l}(M)}\end{bmatrix}} & (22)\end{matrix}$Where E{ } indicates the expectation operator and “H” indicates theHermitian operator. The covariance matrix of the received signal isgiven byσ² =E{r _(z)(k)r _(z)(k)^(H)}  (23)and the minimum mean square estimate is given byσ_(r) ²=σ² −P ^(H) R ⁻¹ P  (24)

The inventor prefers to take advantage of the inherent diversity of theGMSK modulation. To do so, it is preferred to separate the real andimaginary parts of the received signal as if they were independentbranches. Therefore, equation 15 can be re-written as:

                                           (25)${x(k)} = {{\sum\limits_{j = 0}^{M + L - 1}\;{{h_{xfil}(j)}{d\left( {k - j} \right)}}} + {\sum\limits_{l = 1}^{M}\;{w_{lxx}{x\left( {k - l} \right)}}} + {\sum\limits_{l = 1}^{M}\;{w_{lyx}{y\left( {k - l} \right)}}} + {{e_{x}(k)}\begin{matrix}{and} \\{{y(k)} = {{\sum\limits_{j = 0}^{M + L - 1}\;{{h_{yfil}(j)}{d\left( {k - j} \right)}}} + {\sum\limits_{l = 1}^{M}\;{w_{lyy}{y\left( {k - l} \right)}}} + {\sum\limits_{l = 1}^{M}\;{w_{lxy}{x\left( {k - l} \right)}}} + {e_{y}(k)}}}\end{matrix}}}$wherer _(z)(k)=x(k)+jy(k)h _(fil)(j)=h _(xyfil)(j)+jh _(yfil)(j)Note that d(k−1), r(k) and h are now modified to be described inEquation 26 below.

$\begin{matrix}{{{d\left( {k - 1} \right)} = \begin{bmatrix}{d(k)} & {d\left( {k - 1} \right)} & \ldots & {d\left( {k - \left( {M + L - 1} \right)} \right)} & {x\left( {k - 1} \right)} & \; & \; \\{x\left( {k - 2} \right)} & \ldots & {x\left( {k - M} \right)} & {y\left( {k - 1} \right)} & {{y\left( {k - 2} \right)}\mspace{14mu}} & {\ldots\mspace{14mu}} & \left. {y\left( {k - M} \right)} \right\rbrack\end{bmatrix}}{{r(k)} = \begin{pmatrix}{x(k)} \\{y(k)}\end{pmatrix}}{h = \begin{bmatrix}{h_{xfil}(0)} & {h_{yfil}(0)} \\{h_{xfil}(1)} & {h_{yfil}(1)} \\\ldots & \ldots \\{h_{xfil}\left( {M + L - 1} \right)} & {h_{yfil}\left( {M + L - 1} \right)} \\{w_{xxl}(1)} & {w_{lyy}(M)} \\\ldots & \ldots \\{w_{lxx}(M)} & {w_{lyy}(M)} \\{w_{yxl}(1)} & {w_{lxy}(M)} \\\ldots & \ldots \\{w_{lyx}(M)} & {w_{lxy}(M)}\end{bmatrix}}} & (26)\end{matrix}$A joint Minimum Mean Square Estimate (MMSE) is carried out for equation(18) to yield the coefficients in ‘h’.

The inventor prefers to take advantage of the inherent diversity of theGMSK modulation. To do so, it is preferred to separate the real andimaginary parts of the received signal as if they are impendentbranches. Therefore, equation 15 can re-written as:

$\begin{matrix}{{{x(k)} = {{\sum\limits_{j = 0}^{M + K - 1}{{h_{xfil}(j)}{d\left( {k - j} \right)}}} + {\sum\limits_{l = 1}^{M}{w_{lxx}{x\left( {k - l} \right)}}} + {\sum\limits_{l = 1}^{M}{w_{lyx}{y\left( {k - l} \right)}}} + {e_{x}(k)}}}{and}{{y(k)} = {{\sum\limits_{j = 0}^{M + K - 1}{{h_{yfil}(j)}{d\left( {k - j} \right)}}} + {\sum\limits_{l = 1}^{M}{w_{lyy}{y\left( {k - l} \right)}}} + {\sum\limits_{l = 1}^{M}{w_{lxy}{x\left( {k - l} \right)}}} + {e_{y}(k)}}}} & (25)\end{matrix}$wherer _(z)(k)=x(k)+jy(k)h _(fil)(j)=h _(xyfil)(j)+jh _(yfil)(j)Note that d(k−1), r(k) and h are now modified to be described inEquation 26 below.

$\begin{matrix}{{{d\left( {k - 1} \right)} = \begin{bmatrix}{{d(k)}\mspace{14mu}{d\left( {k - 1} \right)}\mspace{14mu}\ldots\mspace{14mu}{d\left( {k - \left( {M + L - 1} \right)} \right)}\mspace{14mu}{x\left( {k - 1} \right)}} \\\left. {{x\left( {k - 2} \right)}\mspace{14mu}\ldots\mspace{14mu}{x\left( {k - M} \right)}\mspace{14mu}{y\left( {k - 1} \right)}\mspace{14mu}{y\left( {k - 2} \right)}\mspace{14mu}\ldots\mspace{14mu}{y\left( {k - M} \right)}} \right\rbrack\end{bmatrix}}{{r(k)} = \begin{pmatrix}{x(k)} \\{y(k)}\end{pmatrix}}{h = \begin{bmatrix}{h_{xfil}(0)} & \vdots & {h_{yfil}(0)} \\{h_{xfil}(1)} & \vdots & {h_{yfil}(1)} \\\ldots & \vdots & \ldots \\{h_{xfill}\left( {M + L - 1} \right)} & \vdots & {h_{yfil}\left( {M + L - 1} \right)} \\w_{xxl} & \vdots & {w_{lyy}(M)} \\\ldots & \vdots & \ldots \\{w_{lxx}(M)} & \vdots & {w_{lxy}(M)} \\{w_{yxl}(1)} & \vdots & {w_{lxy}(M)} \\\ldots & \vdots & \ldots \\{w_{lyx}(M)} & \vdots & {w_{lxy}(M)}\end{bmatrix}}} & (26)\end{matrix}$A joint Minimum Mean Square Estimate (MMSE) is carried out for equation(18) to yield the coefficients in ‘h’.

Note that the above algorithm can run at the sample at the symbol level.In the preferred embodiment, the algorithm runs at an oversamplingfactor of 2 where a tradeoff between the complexity and performance wasmaintained. There preferably should be a decimating block after thefilter block if over-sampling is used.

Detecting Interference Signals

In an alternative embodiment, after separating the desired signal thehypothesized signal r_(d 2)(k) is constructed over the training sequenceor any known signaling part of the burst for some or all trainingsequence codes available for all interfering signals to be separated.The new hypothesized signal r_(d 2)(k) is fed to the separationalgorithm along with the composite received signal to separate thatparticular interferer. The process repeats until all interferers ofinterest are separated.

All separated interferers are fed to a revised (or weighted) channelestimate function separately where a revised value of the channelestimate is obtained. The new revised (or weighted) channel estimationfor the desired signal and each separated interferer is fed to theequalizer, by way of a prefilter, separately for signal detection. Arevised Automatic Frequency Correction (AFC) loop using the detectedbits of the equalizer is used to maintain and to track offset of thedesired signal and each interferer signal.

Increasing the Network Capacity:

In an alternative embodiment the interferers may be used to increase thenetwork capacity. The capacity may be increased by using the samefrequency and time slot (TS) resources but different training codes orknown signaling codes. In this novel approach to Frequency DivisionMultiple Access (FDMA) technology, not only is the interference beingcanceled, but rather the interferers are used as “desired” signals,especially for the Adaptive Multi-Rate Half Rate (AMR HR) or OL(overlay) arrangement.

The main desired signal and all other “auxiliary” signals (referred toin the previous discussion as interferers) are transmitted by the sameBTS or other BTS within the same sector or area of coverage.Conceptually, this is similar to having multiple users using the samefrequency resources simultaneously in the same coverage area, but withdifferent coding schemes. The above algorithm separates the individualsignals. This could be done by hypothesizing the received signal r_(d2)through a rough channel estimate for every auxiliary signal. Afterseparating the signal, a revised channel estimate of the auxiliarysignal is performed as previously discussed, followed by the desiredsignal case and a sequential detection through MLSE. This may be donefor every auxiliary signal. This implementation leads to a networkcapacity increase of multiple folds, clearly unexpected results in amature industry.

Gain Saturation of the Separation Function:

The separation algorithm converges to the desired signal and separatesthe interferers. The separation process works well for low C/I ratiosbecause composite signals comprise a mixture of carriers and moreclosely resemble a Gaussian signal than does a single carrier. For highC/I ratios, that is not the case and convergence of the separationfunction may intermittently slip. Therefore, it is desirable to build acontrol function that examines the C/I ratios and then bypasses theseparation algorithm for high C/I ratios. With a high C/I ratio with adominant desired signal, the C/I estimation is rather straightforwardand a simple finite state machine may be used to choose the signal to bepassed to the equalizer.

Bypassing the Separation Function:

The controller function box 230, shown in FIG. 4, compares the initialand revised (or weighted) C/I estimation and performs the separationfunction when both values are reasonably close and exceed apredetermined threshold (C/I)_(thresh). In an alternative embodiment, ifthe initial channel estimation exceeds a predetermined (C/I)_(thresh),the control switch may bypass the separation and filtering functionaltogether. This may be desired if minimum processing is required toconserve device battery resources.

The Separated Signal:

The desired separated signal is given by:r_(clean)=Sr₁  (27)

The signal is then fed to an MLSE-based equalizer 236 for signaldetection which can, for example, be implemented using a Viterbialgorithm.

Application to Single Antenna Devices:

For single antenna devices, it is possible to hypothesize the receptionof the desired signal to the separation function in the receiver. Thisassumption is valid for all known used wireless modulation techniquesand is not specific to a modulation type. For example, the currentinvention may be used for 4-PSK (and its derivatives QPSK), (QPSK,pi/4-QPSK . . . etc), 8-PSK, QAM-n (n=4, 16, 64) and it is not limitedto real-valued modulation. Note that the algorithm used here is notlimited to inherent diversity gain. However, the Inventor's algorithmmay exploit such diversity gain for such modulations. In that case, aspatio and temporal whitening may be used to optimize the performance.

FIG. 1 illustrates an exemplary GSM wireless system 200 according tothis invention. The wireless system 200 comprises a BTS 220 having atransceiver 201 in communication with a mobile device 203 using antenna211. Some of the components of the mobile device 203 are eithergenerally known in the art or based on those generally known in the art,although functionally some of those components have been modified orenhanced as described herein with respect to the present invention. Themobile device 203 includes a controller 217, such as a microprocessor,microcontroller or similar data processing device that executes programinstructions stored in a memory 204.

The memory 204 may be implemented using any appropriate combination ofalterable, volatile or non-volatile memory or non-alterable, or fixedmemory. The alterable memory, whether volatile or non-volatile, may beimplemented using any one or more of static or dynamic RAM, a floppydisk and disk drive, a writable or re-writable, optical disk and diskdrive, a hard drive, flash memory or other alterable memory componentsknown in the art. Similarly, the non-alterable or fixed memory may beimplemented using any one or more of ROM, PROM, EPROM, EEPROM, anoptical ROM disk, such as a CD-ROM or DVD-ROM disk, and disk drive orother non-alterable memory known in the art.

The controller 217 may be implemented as a single special purposeintegrated circuit (e.g., ASIC) having a main or central processor unitfor overall, system-level control, and separate sections dedicated toperforming various specific computations, functions and other processesunder the control of the central processor unit. The controller 217 canalso be implemented as a single microprocessor circuit, DSP, or aplurality of separate dedicated or programmable integrated or otherelectronic circuits or devices, e.g., hardwired electronic or logiccircuits such as discrete element circuits or programmable logicdevices. The controller 217 also preferably includes other circuitry orcomponents, such as memory devices, relays, mechanical linkages,communications devices, drivers and other ancillary functionality toaffect desired control and/or input/output functions.

The controller 217 is operatively coupled with user interface 208. Theuser interface 208 may include items known in the art, such as adisplay, keypad, speaker, microphone, and other user interface I/Ocomponents. The controller 217 also controls the functions and operationof the SIM card reader 207, which typically is in communication with theSIM card 205 during operation of the mobile device 203. As is known inthe art, the SIM card 205 typically stores information relating to theuser, such as subscribed features, attributes, identification, accountand other information that customizes a mobile device 203 for a typicaluser. The controller 217 also controls and/or monitors the operations ofthe transmitter 215 that transmits radio frequency (RF) signals to thetransceiver 201 of a base station via the antenna 211 coupled to themobile device 203. The controller 217 is also operatively connected toreceiver 213, the functionality of which will be discussed in greaterdetail below.

FIG. 3 illustrates an exemplary embodiment of the receiver 213 accordingto one embodiment of the present invention. The receiver 213 includes amixer 220, a receive (RX) filter 222, a synchronization logic module224, a channel estimator 234, a modulator & interleaver circuit (M/I)242, a pre-filter 244, an equalizer 236, selection switch 255, a decoder238 and a signal conditioner 210.

The receiver 213 uses initial channel estimation and revised channelestimation techniques to reproduce and recreate a received signal r(k)originally transmitted from a transmitting source (i.e., transceiver201, shown in FIG. 1). The method used to calculate the initial andrevised channel estimate employs an interference cancellation algorithmthat uses feedback from previous calculations of interference factors.Accordingly, as more iterations of the interference cancellationalgorithm are made in the receiver 213, the better and more precise eachinterference calculation becomes. However, as will be appreciated bythose skilled in the art, such feedback loops are subject to variousreceive conditions as the mobile device 203 travels and encountersvarious physical terrains, which affect the structure of the receivedsignal r(k). A more detailed explanation of the methods used in thereceiver 213 is described in the following paragraphs.

Assuming there is negligible feedback in a first frame of the receivedsignal r(k) through the receiver 213, the received signal r(k) is passedvirtually unmodified to the RX filter 222, in part because of a lack ofa good estimate of the Doppler frequency (F_(d)). During this firstframe, the output r′(k) of the mixer 220 and the input to the RX filter222 is virtually identical to the received signal r(k) with theexception of a frequency rotation that offsets the Doppler frequency ofthe mobile and any frequency offset between the BTS and handset. Theoffsets F_(d) and F_(offset) are applied to the received signal r(k) tosynchronize the receiver 213 with the transmission bursts from thetransceiver 201. The RX filter 222 is preferably a matched filter thatmatches the transmitter filter and channel condition that passes thedesired frequency band of the received signal r(k) and removes anyaliases and other spurious signal components from the received signalr(k).

Matching the channel condition is accomplished as the received signalr(k) is passed through the RX filter 222. The filtered received signal(r_(filt)) is output from the RX filter 222 and then passed to thesynchronization logic 224. The synchronization logic 224 selects thepreferred sampling point of the filtered received signal (r_(filt)).Accordingly, based of the determined sampling point of the filteredreceived signal (r_(filt)), the synchronization logic 224 may decimatethe filtered received signal (r_(filt)) to a lower sampling rate. If thesubsequent components of the receiver 213 do not require processing at ahigher sampling rate, then the sampling rate of the filtered receivedsignal (r_(filt)) will be the same as the symbol rate. Because thetraining sequence of the desired signal d(k) and its relative positionwithin the received signal r(k) are known, it is possible to estimatethe channel response of the filtered received signal r_(filt). There arevarious techniques known in the art that can be used to determine thestart of the training sequence without departing from the scope of thisinvention. For example, the training sequence code and relativepositions may be determined by calculating the position of the trainingsequence code that gives the highest correlation as the synchronizedsignal r_(i)(k) is correlated with the desired signal d(k). The desiredsignal d(k) represents a hypothesized value of the transmitting signal.

In operation, when the synchronization logic 224 identifies the positionof the training sequence code in the filtered received signal(r_(filt)), the synchronization logic 224 selects the preferred samplingoffset point, among a set of offsets, that best maximizes thecorrelation of the filtered received signal (r_(filt)) with the knowntraining sequence, while minimizing the mean square error. Thissynchronization compensates for the frequency and/or phase shiftingcaused by limited channel effects, time delays, or multipath fading,i.e., the variable patterns that are caused by reflections of thereceived signal r(k) from objects such as cliffs and buildings. As such,a noise plus interference factor (C/I+N) is introduced into the receivedsignal r(k).

A wireless receiver typically estimates the interference and compensatesfor channel distortion adaptively, i.e., the initial channel estimateh_(eff) is revised as each frame of the synchronized signal r_(i)(k) isreceived by the channel estimator 234. The initial channel estimateh_(eff) characterizes the noise plus interference variance estimator(C/I+N) introduced into the received signal r(k) by the path taken bythe received signal r(k), as described above. The C/I+N ratio of thesynchronized signal r_(i)(k) is calculated by the channel estimator 234and fed, along with the initial channel estimate h_(eff), as inputs intothe signal conditioner 210 to provide parameters for calculating controlfunctions used in the signal conditioner 210.

The initial channel estimate h_(eff) indicates, among other things, thereceived signal r(k) relative to the noise level known as the signal tonoise ratio, or C/I, and the level of the multipath fading profilepresent in the synchronized signal r_(i)(k). One of ordinary skill inthe art will recognize that multipath fading is a form of radio fadingcaused by the existence of two or more paths between a transmitter andreceiver. Delays on the reflected path may add to (strengthen) orsubtract from (fade) the strength of the received signal r(k). It shouldbe appreciated that the levels of acceptable multipath fading may varyand may be geographic dependent. Additionally, receivers typicallycompensate for the delay and strength profile based on standards incompliance with specific receiver performance parameters.

As mentioned above, the desired signal d(k) represents a second receivedsignal hypothesized from the received signal r(k). The desired signald(k) as shown in FIG. 3. is obtained from the decoder 238 byreconstructing a version of the received signal r(k). As mentionedearlier, the convolved signal d(k)*h_(eff) is estimated based on thesynchronized signal r_(i)(k).The desired signal d(k) accounts for thememory (time delays) effect of amplitude variations of the channel.

In essence, the signal conditioner 210 applies multi-receiver signalinterference cancellation to a SAIC receiver. The signal conditioner 210uses the input variables (C/I+N) and the initial channel estimateh_(eff) as threshold quantities to apply appropriate filtering to thesynchronized signal r_(i)(k) and the convolved signal d(k)*h_(eff) toproduce a revised signal r_(r)(k). The revised signal r_(r)(k) isrepresentative of an approximation of the received signal r(k) as afunction of the synchronized signal r_(i)(k) and the convolved signald(k)*h_(eff). The revised signal r_(r)(k) is output to the prefilter244.

The prefilter 244 is preferably a minimum-phase filter. Due to thespread characteristics of the received signal r(k) resultant from C/I+Nintroduced into the signal, the revised signal r_(r)(k) is preferablycompacted prior to equalization by the equalizer 236. The prefilter 244outputs a filtered r_(f)(k), signal wherein the energy of the spectralcomponents of the revised signal r_(r)(k) have been compacted to fallwithin the symbol rate of the equalizer 236, such that the relevantcomponents of the revised signal r_(r)(k) may be used. This approach mayalso act to reduce the complexity of the equalizer 236.

The prefilter 244 demodulates the received signal using the revisedchannel estimate h_(eff2) and the separated signal r_(clean), oralternatively, the filtered separated signal r_(fclean) as the case maybe, to enhance the received signal based on the revised channel estimateh_(eff2) or the weighted channel estimate h_(w). Thus, the resultingsignal output to the equalizer 236 has had its associated interfererscanceled and represents a high quality reproduction of the originaltransmitted signal from the transceiver 201.

The equalizer 236 is generally known in the art and may be characterizedas a MLSE (Most Likelihood Sequence Estimator). The equalizer 236preferably uses a Viterbi algorithm to manipulate the filtered signalr_(f)(k) and the channel estimate output from the switch 255.

A MLSE calculation is then applied to the signal. The MLSE locallygenerates all possible representations of the filtered signal r_(f)(k)based on all of the possible transmitted sequences, and then comparesthese estimates to the filtered signal r_(f)(k) that is actuallyreceived. As will be appreciated by those skilled in the art, thelocally generated signal that most closely matches the received signalindicates what is the most likely transmitted sequence.

The output of the equalizer 236 includes softvalues of the filteredsignal r_(f)(k). The MLSE is an optimal detector in the sense ofminimizing the accumulated detected errors for the transmitted sequence.The softvalues are fed to the decoder 238 to provide a series of decodedbits/symbols.

Due to the complexity of EDGE, it is preferable that a full Viterbicalculation not be performed in the equalizer 236. Rather a moreappropriate, albeit sub-optimal algorithm, is preferably employed, suchas RSSE (Reduced State Sequence Estimator) or DFSE (Decision FeedbackSequence Estimator) which, in the inventor's view, generally provides anacceptable trade-off between accuracy and complexity. The softvalues fedto the decoder 238 are generated based a Viterbi algorithm calculatingthe states of the filtered signal r_(f)(k).

Additionally, from the equalizer 236, the estimated Doppler and offsetfrequency (F_(d) & F_(offset)) of the non-dominant interferers are fedback to the mixer 220. The process used to find the estimated Dopplerand offset frequencies is common and well known in the art. The Dopplerand offset frequencies represent the apparent change in the receivedfrequency due to the relative motion and rotation of the transceiver 201and by providing and mixing such feedback with the received signal r(k),enhancing the second and subsequent calculations of the receiver 213.The frequency offset may also be estimated during the channel estimationphase in channel estimator 234 such that the overall minimum mean squareand the channel estimate are jointly minimized. The decoder 238 producesa probability estimation of the softvalues output from the equalizer 236to scale the output-softvalues to a more accurate (±1V) line=decodedrepresentation. Finally, the decoder 238 scales the line-decodedrepresentation to a binary representation of the signal to produce thefinal decoded bits/symbols. These decoded bit/symbols are sent asinformation data to the handset to be output to the user. The decodedbit/symbols of the decoder 238 are also input into the M/I 242.

The M/I 242, as known in the art, uses a data mixing technique to reducethe number of undetected error bursts. In the interleaving process, thedecoded bit/symbols are reordered in such a manner that any twosuccessive symbols are separated by n−1 symbols in the sequence, whereinn is the degree of interleaving to produce the desired signal d(k). Thedesired signal d(k) is finally ordered into its original sequence by thesignal conditioner 210, after being convolved with the channel estimateh_(eff), as discussed above. Thus, the errors (in time) are effectivelyspread or randomized to enable a more complete correction by a randomerror-correcting code used in the signal conditioner 210.

In all subsequent frames of data passed through the receiver 213, theresulting output of the decoder 238 becomes stronger and more accuratedue to the iterative nature of the receiver 213 and the feedback loopspresent.

It should be understood that the receiver 213 of FIG. 3 and FIG. 4 maybe, in general, implemented using a variety of hardware and/or softwarecomponents. For example, the signal separator 228 may use a number ofdifferent commonly known communications components, such asdemodulators, mixers, filters, and analog-to-digital (A/D) converters.In general, such components may be implemented using hardware such asdiscrete circuit components, hybrid circuits and application-specificintegrated circuits (ASICs), and/or combinations of such hardware andsoftware or firmware configured to execute on special-purpose processingdevices or general-purpose processing devices such as microprocessors,microcontrollers and digital signal processor (DSP) chips. The equalizer236 may similarly be implemented using special-purpose hardware such asgate arrays or ASICs, software or firmware executing on special purposeprocessing devices or on general purpose processing devices such asmicroprocessors, microcontrollers or DSP chips, or combinations thereof.

As shown in FIG. 4, one embodiment of the signal conditioner 210preferably contains switches 240, 250 and 255, signal separator 228, acontroller 230, a spatial and temporal whitening filter (S/T) 232 and arevised channel estimator 235.

In operation, the controller 230 manages the positions of switches 240,250 and 255. The controller 230 provides control functions to theswitches 240, 250 and 255 based on the C/I ratios contained in the inputchannel estimates h_(eff) and h_(eff2). The possible switch settings andsignal treatment based on the C/I ratios are discussed below:

Low C/I Ratio

When low C/I ratios are detected in the synchronized signal s_(i)(k), asignal separation may be advantageously performed. Thus, based onthreshold parameters of the calculated C/I ratios from the channelestimator 234, the controller 230 will position the switch 250 in the“A” position, and the switch 240 will also be placed in the “A”position. While the switch 240 is in the “A” position, the synchronizedsignal r_(i)(k) is passed to the signal separator 228.

With respect to determining the position of switch 240, the control 230compares the initial C/I ratio from the initial channel estimator 234with a weighted C/I ratio from the S/T filter 232. The weighted C/Iratio is a second estimate of the channel response of the cleaned signalr_(clean.)

The signal separator 228 performs a separation function using theseparation algorithms previously discussed. The separation algorithm,using the convoluted signal d(k)*heff as one input and the synchronizedsignal r_(i)(k) as a second input, outputs the separated signalr_(clean) with the C/I+N removed or significantly diminished. Theseparated signal r_(clean) represents a processed clean version of thedesired signal d(k), wherein all C/I+N interferers of interest have beenremoved as determined by the receiver and based on the received signalr(k) and the other parameters set forth above. A more preciseexplanation of this switching process and the results achieved isdescribed with respect to FIGS. 5 and 6.

From the signal separator 228, all separated interferers I_(s) are fedto the S/T filter 232 separately, such that a weighted channel estimateh_(w) of the initial channel estimate h_(eff) may be calculated. Theweighted channel estimate h_(w) is fed into the prefilter 244. As willbe appreciated by those skilled in the art, because of the iterativenature of the algorithms running on the receiver, the weighted channelestimate h_(w), similar to the revised channel estimate represents amore precise version of the initial channel estimate h_(eff).

Those skilled in the art will recognize that the S/T filter 232 may beany known or conventional filtering of the spatial and temporalwhitening component as is commonly present in wireless transmissions,however an exemplary embodiment discussed later, an exemplary filter isdiscussed.

High C/I Ratio

If the interference contribution is considered negligible ornon-existent (i.e., high C/I ratio), a signal separation does not needto be performed. In other words, when the values of the C/I ratios arehigh and exceed a predetermined threshold, the signal separator 228 andthe S/T filter 232 are bypassed. In such a case, the switch 240 will bein position B. In this case, signal quality of the synchronized signalr_(i)(k) and the convolved signal d(k)*h_(eff) is considered good. Whilethe switch 240 is in position B, the synchronized signal r_(i)(k) ispassed unchanged through the switch 240 to the prefilter 244.Accordingly, the switch 250 also is in position “B”.

In this high C/I ratio case, a revised channel estimate h_(eff) iscalculated. The synchronized signal r_(i)(k) is processed in a similarmanner to the separated signal r_(clean), as discussed above.

The switch 255 is controlled by controller 230 according to thepositions of switches 240 and 250. For example, at a high C/I ratio, theswitch 255 is set to position “B” to input the weighted channel estimateh_(w) into the equalizer 236.

FIGS. 5 and 6 represent exemplary performance results from the receiver213 based on algorithm modeling. These results are favorable and in viewof the current teachings in the art, are also quite unexpected. In FIGS.5 and 6 the performance curves relate a C/I ratio to a Raw BERpercentage from use of the receiver 213 and are compared to thoseobtained using a conventional receiver for GMSK modulation using thesame interference profile. It is clear from FIGS. 5 and 6 that thereceiver 213 exhibits a robust performance increase over conventionalreceivers for low and high C/I ratios for a single interferencescenario, as the case in FIG. 5, and across a wide range of DIR, as thecase in FIG. 6. For ease of illustration in FIGS. 5 and 6, theperformance curves of receiver 213 are labeled as “SAIC receiver”. InFIGS. 5 and 6, it is seen that the relative link level SAIC gaindifference between the conventional receiver and the receiver 213 issizeable and unexpected.

FIG. 5 represents exemplary results output from the receiver 213 forsingle interferer rejection. At C/I ranges from −10 dB to +14 dB, thereceiver 213 is sizably more effective in reducing the amount of BERpresent in the representative signal. In some instances, the receiver213 exhibits more than one order of magnitude improvement in BEReffectiveness.

In FIG. 6, performance is shown as a function of C/I for a conventionalreceiver, and as a function of C/I and DIR for the SAIC receivers. Thismodel consists of mirrors the interference profile designated in 3GPP[See 3GPP Tdoc GAHS-030025, “Exemplary Link Level Assumption,Configurations ⅔”, SAIC Rapporteur]; wherein five interferers are isused to characterize the link level performance of the receiver 213.

The interferers and other key assumptions are defined as follows:

Two Co-Channel Interferers;

One residual co-channel noise, which is based on multiple co-channels;

One adjacent channel interferer;

One residual adjacent noise, which is based on multiple adjacentchannels; and

White Gaussian Noise.

It was determined that the average individual ratios of the dominantco-channel interferers to each of the other interferers was relativelyconstant over the range of C/I expected, and thus, the profiles aredefined in terms of these ratios.

As shown in FIG. 6, as the value of the C/I ratio increases from −10 dBto +10 dB, the performance curves of the conventional receiver begin toconverge with the performance curves of the receiver 213. Based onstudies to date, the performance of the conventional receiver approachesthat of the receiver 213. For example, in DIR=2 dB case, at C/I ratioshigher than approximately 3 dB, and in the 10 dB case, at C/I ratioshigher than 7 dB, the conventional receiver's performance may surpassthat of the receiver 213. Accordingly, at such values, it may beadvantageous to bypass the SAIC functions to achieve better performanceand increase processing efficiencies. For this reason, in a preferredembodiment of the receiver 213, switch 240 was included, which whenplaced in position B, the signal separator 228 is bypassed.

An exemplary S/T filter, according to the invention disclosed herein andas discussed above, is shown in FIG. 7. The S/T filter shown in FIG. 7can be characterized as an adaptive filter. The aim of the adaptive (S/Tfilter) filter is to iteratively adapt the filter coefficients 700 suchthat the error sequence of the power of the covariance in theinterference and noise is as close to zero as possible, within somevalue of tolerance. The S/T filter shown in FIG. 7 uses previousmeasurements of the real (x(k−M) . . . x(k−2) . . . , etc.) andimaginary (y(k−M) . . . y(k−2) . . . , etc.) symbols to predict thevalues of the current symbols of the real (x(k)) and imaginary (y(k))components of the weighted signal r_(w). Due to the relationship betweenthe real and imaginary components (x(k) and y(k)) present in theweighted signal r_(w), the real function 705 and the imaginary function710 are crossed-coupled, such that the previous values of the realcomponents impact the values of the imaginary components and theprevious values of the imaginary components impact the real components.

In design, the S/T filter must have an order (M) that is large enough toaccurately predict the correlation between adjacent real and imaginarysymbols. As the order (M) of the filter is successively increased, thecorrelation between adjacent real and imaginary symbols is reduced untilthe S/T filter produces a sequence of uncorrelated real and imaginarycomponents (x(k) and y(k)). Accordingly, the whitening of the inputsignal is accomplished to produce the weighted signal r_(w).

FIGS. 8 and 9 illustrate an exemplary method for interference canceling,according to this invention. The method begins at step S100 and proceedsto step S200 wherein a transmitted signal is received from a transceiverand mixed with a previously measured Doppler and Offset frequency. TheDoppler and Offset frequencies represent the apparent change in thereceived frequency due to the relative motion of the transceiver. Theprocess then continues to step S300.

At step S300, the received signal is filtered using a matching filter tocorrect the appropriate frequency band of the received signal. Thefilter also removes any unnecessary signal components from the receivedsignal due to co-channel interference, bleeding, phase shifts, widebandinterference, etc. Upon completion of the filtering, the filter outputsa filtered received signal to a synchronizer. The process then continuesto step S400.

At step S400, the synchronizer determines the training sequence of aninterferer and its relative position within the filtered receivedsignal. The training sequence and its relative position are determinedby the position that gives the highest correlation as the correctedsignal is compared to a desired signal. Once the position of thetraining sequence is known, the synchronizer applies the appropriateoffset frequencies to the signal to produce a synchronized signal. Theprocess then proceeds to steps S500 and S1300 simultaneously.

At step S1300, an initial channel estimate is performed on thesynchronized signal. The process then proceeds to step S1400, where theC/I ratio of the initial channel estimate is calculated. Once the C/Iratio is calculated it is input into a controller (S500). The processthen continues to step S1500.

At step S1500, a convolution of the desired signal and the initialchannel estimate is calculated. The convolved signal is input into asignal separator for use in producing a separated signal in step S1000.

In step S500, the C/I ratios from the initial channel estimate, theweighted channel estimate and the revised channel response are compared,as necessary, to determine whether they are within a predeterminedthreshold at step S600. If the ratios are within the predeterminedthreshold, the process continues to step S700. Otherwise, the processjumps to step S1000.

At steps S700 and S800, the signal separation and white filteringprocesses are bypassed in favor of conventional separation methods. Theprocess then continues to step S900.

At step S900, a revised channel estimate is calculated. The revisedchannel estimate is fed back to the controller for comparison with theinitial channel estimate or the weighted channel estimate, as necessary.The process continues to step S1600, as illustrated in FIG. 9.

Finally, at step S1600 the signal, whether it is a separated signal orthe synchronized signal (due to the position of switch 250) and therevised channel estimate are passed to the prefilter at step S1800.

At step S1000, a signal separation is performed based on the input fromthe convolution of the desired signal and the initial channel response(S1500). The process then continues to step S1100.

At step S100, white and spatial filtering is performed on the separatedsignal to output a weighted signal. At step S1200, a weighted channelestimate is performed on the weighted signal. The process then continuesto step S1700.

At step S1700, the weighted signal and the weighted channel estimate areoutput to a prefilter at step S1800.

At step S1800, the prefilter compacts the weighted or revised signal, asthe case may be (for example, depending on the position of switch 250 ofFIG. 4), to remove the effects of the spread characteristics resultantfrom C/I introduced into the signal. The process then continues to stepS1900.

At step S1900, where an MLSE step is performed using Viterbicalculations and a branch metric is performed to trace back the originalstates of the received signal. From this trace back step, at step S2000,softvalues are output to a decoder.

Additionally, at step S1900 the estimated Doppler and Offset frequenciesof the non-dominant interferers are fed back to a mixer to be used instep S2000. The process then continues to step S2100.

At step S2100 the softvalues are decoded to produce the detectedbits/symbols. The process then continues to step S2200, where thedetected bits/symbols are output to a modulator and interleaver. Theprocess then continues to steps S2300 and S2400, wherein the desiredsignal is calculated from the detected bits/symbols and is output to aconvolution function (step S1500) where a convolution of the desiredsignal and the initial channel estimate is performed.

The process finally ends at step S2500.

Although the process outlined in FIGS. 8 and 9 are described with aspecific order of operation, one of ordinary skill will recognize thatthe order of many of the processes can be rearranged without departingfrom the scope of the invention. For example, with respect to FIG. 8,the process automatically bypasses the white and spatial filtering ofthe synchronized signal as a result of the separation step beingbypassed. However, is should be recognized that the white and spatialfiltering may be performed should the synchronized signal containspatial or white noise.

The invention as embodied herein has been applied to the voice channelas an example. Those skilled in the art will recognize that thetechniques and processes of the present invention may be used in otherparts of a cellular system and applied to other types of wirelessdevices. For example, other modes of operation may include: 1)manipulating the interference cancellation for all logical channelstraffic channels (TCH) and associate control channels (ACCH) of awireless network; 2) employing the embodiments disclosed herein(interference cancellation) for the ACCH channel and not the trafficchannel. (This is typically the case when operating in robust lowercodec mode (4.75 and 5.9 codec) and the signaling is not as robust.);and 3) employ the embodiments disclosed herein (interferencecancellation) for TCH and not ACCH. (This is typically the case whenoperating in less robust mode such as 12.2 and the signaling isrelatively robust.)

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered ad exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1. A method for suppressing interference received by an antenna,comprising: receiving a first received signal at the antenna;hypothesizing a second received signal; separating an interfering signalfrom a desired signal using the first received signal and thehypothesized second received signal; and filtering the desired signal;wherein the filtering step comprises filtering through a multi-stageadaptive predictive filter having a real component and an imaginarycomponent, and wherein an output of the imaginary component also servesas an input into a subsequent stage of the real component and wherein anoutput of the real component also serves as an input into a subsequentstage of the imaginary component.
 2. The method of claim 1, wherein thesecond received signal is hypothesized using a training sequence code.3. The method of clam 2, further comprising: estimating a channelresponse based on the first received signal; and wherein the trainingsequence code has an expected value and the first signal has an observedtraining sequence code associated therewith and the estimating step isbased on the expected value of the training sequence code and theobserved training sequence code.
 4. The method of claim 3, wherein theestimating step comprises a convolution of a transmitted filterresponse, received filter response, and a media response in accordancewith the following equation:h _(eff)(n)=tx(n)*C(n)*rx(n), wherein tx(n) is the transmitter filterresponse, rx(n) is the receiver filter response, C(n) is the mediaresponse, and wherein h_(eff)(n) is a channel impulse response.
 5. Themethod of claim 1, wherein the separating step comprises an applicationof the central limit theorem.
 6. The method of claim 5, wherein thecentral limit theorem is implemented in accordance with the following: anext value of${{s_{p}\left( {m + 1} \right)} = {{s_{p}\left( {m + 1} \right)} - {\sum\limits_{j = 1}^{P}{s_{j}s_{j}^{H}{s_{p}\left( {m + 1} \right)}}}}},$where s_(p)(m+1) is an estimated vector, m, j, and p are integers, s_(j)are previously found orthogonal vectors, and s_(j) ^(H) is the conjugatetransposeof s_(j).
 7. The method of claim 1, wherein the separating stepis selectively performed according to a carrier to interference (C/I)ratio being below a predetermined level.
 8. The method of claim 7,further comprising selectively filtering the desired signal.
 9. Themethod of claim 8, wherein the separating step is selectively performedas a function of the C/I ratio as applied to an initial channel estimateand a weighted C/I ratio as determined in the filtering step.
 10. Themethod of claim 8, wherein the filtering step and the separating stepare performed when C/I ratios fall below the predetermined level. 11.The method of claim 1, wherein the hypothesized second received signalis hypothesized using an expected value of the first received signal.12. The method of clam 11, wherein the first signal has an observedvalue associated therewith and the estimating step is based on theexpected value and the observed value.
 13. A method for suppressinginterference received by an antenna, comprising: receiving a firstreceived signal at the antenna; hypothesizing a second received signal;separating an interfering signal from a desired signal using the firstreceived signal and the hypothesized second received signal; selectivelyfiltering the desired signal; and wherein the filtering step comprisesfiltering through a multi-stage adaptive predictive filter having a realcomponent and an imaginary component, and wherein an output of theimaginary component also serves as an input into a subsequent stage ofthe real component and wherein an output of the real component alsoserves as an input into a subsequent stage of the imaginary component.14. The method of claim 13, wherein the filtering step is selected basedon a carrier to interference (C/I) ratio.
 15. The method of claim 14,wherein the filtering step is selected when the C/I ratio falls below apredetermined threshold.
 16. The method of claim 13, wherein thefiltering step is selected based on a low C/I ratio as applied to asynchronized signal.
 17. A method comprising: receiving a transmittedsignal at a receiver as a first received signal; hypothesizing anotherreceived signal based at least in part on a predefined transmit sequenceassociated with the first received signal; separating one or moreinterfering signals from the first received signal using the firstreceived signal and the hypothesized other received signal; andselectively filtering the desired signal through a multi-stage adaptivepredictive filter supplying a real component and an imaginary component,and wherein previous values of imaginary components serve as an input indetermination of a current real component and wherein previous values ofreal components serve as an input in determination of a currentimaginary component.
 18. The method as recited in claim 17, wherein thepredefined transmit sequence is associated with a training sequencecode.
 19. The method as recited in claim 17 wherein hypothesizing theother received signal comprises: convolving data d(k) with an estimatedchannel response (h_(eff)) to form d(k)*h_(eff) as the hypothesizedother received signal.
 20. The method as recited in claim 19 whereinhypothesizing the other received signal further comprises: decoding databits associated with the first received signal; and interleaving andmodulating the decoded data bits to form the data d(k) corresponding totransmitted data.